Combining regression trees and panel regression for exploring and testing the impact of complementary management practices on short-notice elective operation cancellation rates.
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ABSTRACT:
SUBMITTER: Salehnejad R
PROVIDER: S-EPMC7738292 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectivesElective tracheotomy (ET) procedures in intensive care units (ICU) might be different in accordance with countries and ICUs' features. The aim of the present study was to search the epidemiology of ET procedures in Turkey.MethodsA questionnaire which consists of 43 questions was sent by e-mail to 238 ICUs which were officially recognized by The Turkish Ministry of Health. All answers were obtained between August 1, 2015 and August 31, 2015.ResultsTwo hundred and three ICUs (85.3%) participated in this study. 177 (87.2%) and 169 (83.4%) of ICU's were level III and mixed ICUs respectively. Anesthesiologists were the director of 189 (93.0%) ICUs. Estimated total count of admitted, mechanically ventilated and tracheotomized patients in 2014 were 126282, 80569 (63.8%) and 8989 (7.1%) respectively. Most common indication for ET was prolonged mechanical ventilation (76.9%). The first choice for ET procedure was percutaneous in 162 (79.8%) ICUs. Griggs guide wire dilatational forceps (GWDF) technique was used as the first choice for elective percutaneous tracheotomy (EPT) by 143 (70.4%) ICUs. Most common early EPT complication was bleeding (68.0%) and late EPT complication was stenosis (35.0%). While facilitation of weaning was most important advantage (26.1%), bleeding and tracheal complications were most important disadvantages for EPT (29.1%).ConclusionsMost common indications for ET are prolonged MV and coma in Turkish ICUs. EPT is the preferred procedure for ET and GWDF is the most common technique. Bronchoscopy and USG are rarely used as a guide.
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Project description:OBJECTIVE: To evaluate our unit's theatre throughput efficiency, to identify where inefficiencies existed and consequently where the greatest improvement might be made.To identify the causes of day of surgery cancellations and how they might be avoided. PATIENTS AND METHODS: A prospective audit of theatre utilisation was undertaken over a 6 month period between 05/02//2013 and 02/08/2013 at Ipswich General Hospital, QLD, Australia.TIMES COLLECTED WERE: time of patient arrival in anaesthetic bay, start time of operative procedure, end time of operative procedure, and time of patient leaving theatre.The causative factors for any delays or day of surgery cancellations were identified and recorded where possible. RESULTS: In the six month period 26,850 sessional minutes were available for elective operating over 100 operating sessions.304 elective cases were performed, split between 21 major and 283 minor proceduresThe sessions ran overtime a cumulative 2114 minutes.Total non-operative minutes totalled 13,209 (50.3% of all available time), split between late starts 499 minutes (1.8%), early list finishes 1894 minutes (7.05%), changeover time 1869 minutes (6.9%) and anaesthetic time, 8974 minutes (33.4%)Actual operating time only compromised 50.7% of all available elective operating session time (13,614 minutes)Theatre utilisation was 91.8%.51 procedures were cancelled on the day of surgery during the audit period, representing 14.3% of all scheduled procedures.The most common reason for cancellation was lack of surgical fitness, followed by inadequate operative time. CONCLUSION: A significant proportion of all elective operative time was consumed by non-operative minutes.Inefficiencies existed in turnover of patients as well as over as well as underbooking of patients on elective lists.An excessive number of cases were cancelled on the day of surgery, wasting valuable operative time.A multi-parametric approach must be taken to improve operation list utilisation.
| S-EPMC4184311 | biostudies-literature
Project description:PurposeHealth care systems in most European countries were temporarily restructured to provide as much capacity as possible for the treatment of coronavirus disease 2019 (COVID-19) patients. Subsequently, all elective surgeries had to be cancelled and postponed for months. The aim of the present study was to assess the pretreatment health status before and after COVID-19-related cancellation and the psychosocial distress caused by the cancellation.MethodsFor this study, a questionnaire was developed collecting sociodemographic data and information on health status before and after the cancellation. To assess psychosocial distress, the validated depression module of the Patient Health Questionnaire (PHQ-9), was implemented. PHQ-9-Scores of 10 and above were considered to indicate moderate or severe depressive symptoms. In total, 119 patients whose elective orthopaedic surgery was postponed due to the COVID-19 pandemic were surveyed once at least 8 weeks after the cancellation.ResultsSeventy-seven patients (65%; 34 female, 43 male) completed the questionnaire and were included. The predominant procedures were total knee arthroplasty (TKA), hip arthroscopy and foot and ankle surgery. The mean pain level significantly increased from 5.5 ± 2.2 at the time of the initially scheduled surgery to 6.2 ± 2.5 at the time of the survey (p < 0.0001). The pain level before cancellation of the surgery was significantly higher in female patients (p = 0.029). An increased analgetic consumption was identified in 46% of all patients. A mean PHQ-9 score of 6.1 ± 4.9 was found after cancellation. PHQ-9 scores of 10 or above were found in 14% of patients, and 8% exhibited scores of 15 points or above. Significantly higher PHQ-9 scores were seen in female patients (p = 0.046). No significant differences in PHQ-9 scores were found among age groups, procedures or reasons for cancellation.ConclusionCancellation of elective orthopaedic surgery resulted in pain levels that were significantly higher than when the surgery was scheduled, leading to increased analgesic use. Additionally, significant psychosocial distress due to the cancellation was identified in some patients, particularly middle-aged women. Despite these results, confidence in the national health care system and in the treating orthopaedic surgeons was not affected.Level of evidenceLevel III.
| S-EPMC7952835 | biostudies-literature
Project description:BackgroundCancellation of elective surgical operation recognized as a major cause of emotional trauma to patients as well as their families. In Ethiopia, prevalence and root causes for elective surgical case cancellation varies from time to time in different settings. This systematic review and meta-analysis aimed to find the pooled prevalence and root causes for elective surgical case cancellation in Ethiopia.MethodsThe databases for the search were Web of Science, PubMed, and Google Scholar. The last literature search was performed on February 8, 2020. To assess publication bias Egger's regression analysis was applied. The pooled estimation was estimated using random-effects model meta-analysis. Subgroup analysis was also done based on the root causes of surgical case cancellation.ResultsThis meta-analysis included a total of 5 studies with 5591 study participants. The pooled prevalence of elective surgical case cancellation was 21.41% (95% CI: 12.75 to 30.06%). Administration-related reason (34.50%) was the most common identified root cause, followed by surgeon (25.29%), medical (13.90%), and patient-related reasons (13.34%).ConclusionThe prevalence of elective surgical case cancellation was considerable. The most common root cause for elective surgical case cancellation was administration-related reasons, followed by the surgeon, medical and patient-related reasons. The causes for the surgical cancellations are potentially preventable. Thus, efforts should be made to prevent unnecessary cancellations through careful planning.
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Project description:This paper proposes a novel paradigm for building regression trees and ensemble learning in survival analysis. Generalizations of the CART and Random Forests algorithms for general loss functions, and in the latter case more general bootstrap procedures, are both introduced. These results, in combination with an extension of the theory of censoring unbiased transformations applicable to loss functions, underpin the development of two new classes of algorithms for constructing survival trees and survival forests: Censoring Unbiased Regression Trees and Censoring Unbiased Regression Ensembles. For a certain "doubly robust" censoring unbiased transformation of squared error loss, we further show how these new algorithms can be implemented using existing software (e.g., CART, random forests). Comparisons of these methods to existing ensemble procedures for predicting survival probabilities are provided in both simulated settings and through applications to four datasets. It is shown that these new methods either improve upon, or remain competitive with, existing implementations of random survival forests, conditional inference forests, and recursively imputed survival trees.
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Project description:BackgroundNovel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities.ResultsWe propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database) is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods.ConclusionsREGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear regressions to separate areas of the search space favoring to infer localized similarities over a more global similarity. Furthermore, experimental results show the good performance of REGNET.
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Project description:BackgroundMissing data are common when analyzing real data. One popular solution is to impute missing data so that one complete dataset can be obtained for subsequent data analysis. In the present study, we focus on missing data imputation using classification and regression trees (CART).MethodsWe consider a new perspective on missing data in a CART imputation problem and realize the perspective through some resampling algorithms. Several existing missing data imputation methods using CART are compared through simulation studies, and we aim to investigate the methods with better imputation accuracy under various conditions. Some systematic findings are demonstrated and presented. These imputation methods are further applied to two real datasets: Hepatitis data and Credit approval data for illustration.ResultsThe method that performs the best strongly depends on the correlation between variables. For imputing missing ordinal categorical variables, the rpart package with surrogate variables is recommended under correlations larger than 0 with missing completely at random (MCAR) and missing at random (MAR) conditions. Under missing not at random (MNAR), chi-squared test methods and the rpart package with surrogate variables are suggested. For imputing missing quantitative variables, the iterative imputation method is most recommended under moderate correlation conditions.
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